基于RGB图像的冠层尺度水稻叶瘟病斑检测与抗性评估

doi:10.3785/j.issn.1008-9209.2021.05.131

浙江大学学报（农业与生命科学版）

2021, Vol. 47

Issue (4): 415-428 DOI: 10.3785/j.issn.1008-9209.2021.05.131

作物表型分析技术及应用专题

基于RGB图像的冠层尺度水稻叶瘟病斑检测与抗性评估

谢鹏尧^1,²(

),富昊伟³,唐政^1,²,麻志宏^1,²,岑海燕^1,²(

)

^1.浙江大学生物系统工程与食品科学学院，杭州 310058
^2.农业农村部光谱检测重点实验室，杭州 310058
^3.嘉兴市农业科学研究院，浙江嘉兴 314016

RGB imaging-based detection of rice leaf blast spot and resistance evaluation at the canopy scale

Pengyao XIE^1,²(

),Haowei FU³,Zheng TANG^1,²,Zhihong MA^1,²,Haiyan CEN^1,²(

)

^1.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
^2.Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
^3.Jiaxing Academy of Agricultural Sciences, Jiaxing 314016, Zhejiang, China

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摘要：

针对依赖人工主观判断水稻叶瘟抗性费时费力且准确率低的问题，本文提出了一种基于水稻冠层尺度RGB图像和掩膜区域卷积神经网络（mask regions with convolutional neural network, Mask-RCNN）深度学习框架的水稻叶瘟病斑识别检测方法，通过分析水稻RGB图像中不同类别病斑的数量信息，构建多种分类模型来评估病斑数量和抗性水平之间的关联性。首先采集包括粳稻品系、早籼品系和籼型恢复系等不同品系的水稻育种材料的苗期RGB图像，然后通过对输入图像进行预处理和标记，最终建立了用于识别水稻叶瘟病斑的Mask-RCNN模型，实现了叶瘟病斑的矩形框检测、掩膜分割和分类，其平均交并比（mean intersection over union, mIoU）为0.603。当采用0.5的交并比（intersection over union, IoU）阈值时，测试数据集的病斑检测平均准确率均值（mean average precision, mAP）为0.716。在基于病斑数量的抗性评估模型中，高斯过程支持向量机在测试数据集上取得了94.30%的最高抗性评估准确率。研究结果表明，基于水稻冠层RGB图像和Mask-RCNN模型可实现水稻叶瘟病的准确识别，检测的病斑数量特征和叶瘟抗性水平高度相关。本研究为水稻抗病性品种的高效选育提供了技术支撑。

关键词： 冠层尺度; 水稻病害; 叶瘟检测; 深度学习; 抗性评估

Abstract:

Visual inspection of rice leaf blast resistance is time-consuming and labor-intensive with low accuracy. Therefore, this study aims to identify and detect rice leaf blast spots based on RGB imaging of rice canopy combined with mask regions with convolutional neural network (Mask-RCNN), and develop multiple classification models to quantify the number of disease spots and evaluate the association between the number of disease spots and the resistance level by analyzing the quantitative information of different categories of disease spots in RGB images of rice. First, we collected RGB images from different rice breeding lines at the seedling stage, including japonica lines, early indica lines and indica recovery lines. Preprocessing and labeling of the input images were then performed. A Mask-RCNN model for the recognition of rice leaf blast spots was developed to perform the rectangular frame detection, mask segmentation and classification. The classification result of rice leaf blast spots with the mean intersection over union (mIoU) of 0.603 was achieved. The mean average precision (mAP) of the test dataset was 0.716, when the intersection over union (IoU) threshold of 0.5 was used. Among all the classification models, Gaussian process support vector machine obtained the highest prediction accuracy of 94.30% (proportion of disease spots in each category corresponding to different resistances) on the test dataset. The above results demonstrate that RGB images of rice canopy combined with Mask-RCNN have the great potential for the accurate identification of rice leaf blast spots, and the number of detected disease spots is highly correlated with the rice leaf blast resistance level. The proposed method is promising for efficient selection of disease-resistant rice varieties in breeding.

Key words: canopy scale rice disease leaf blast detection deep learning resistance evaluation

收稿日期: 2021-05-13 出版日期: 2021-09-02

CLC:

TP 751

基金资助: 浙江省重点研发计划(2020C02002);浙江省嘉兴市科技计划项目应用性基础研究专项(2019AY11006)

通讯作者: 岑海燕 E-mail: pyxie@zju.edu.cn;hycen@zju.edu.cn

作者简介: 谢鹏尧（https://orcid.org/0000-0002-1014-5399），E-mail：pyxie@zju.edu.cn

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引用本文:

谢鹏尧,富昊伟,唐政,麻志宏,岑海燕. 基于RGB图像的冠层尺度水稻叶瘟病斑检测与抗性评估[J]. 浙江大学学报（农业与生命科学版）, 2021, 47(4): 415-428.

Pengyao XIE,Haowei FU,Zheng TANG,Zhihong MA,Haiyan CEN. RGB imaging-based detection of rice leaf blast spot and resistance evaluation at the canopy scale. Journal of Zhejiang University (Agriculture and Life Sciences), 2021, 47(4): 415-428.

链接本文:

http://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2021.05.131 或 http://www.zjujournals.com/agr/CN/Y2021/V47/I4/415

图1 原始图像和分割图像示例红色实线框表示1 500×1 500裁剪区域；蓝色虚线框内的图片表示相机视野覆盖栽种单一品系的区块；其他未被框选的图片表示相机聚焦的局部病斑。

图2 水稻叶瘟病斑类型A.慢性型病斑；B.急性型病斑；C.褐点型病斑；D.白点型病斑。

图3 数据处理流程

图4 Mask-RCNN模型架构FPN：特征金字塔网络；RPN：区域生成网络；RoI：感兴趣区域。

图5 Mask-RCNN损失函数变化曲线

图6 Mask-RCNN预测结果示例矩形虚线框和掩膜为模型生成结果，每个结果上都有病斑类别预测结果和置信度。

图8 Mask-RCNN模型输出部分可视化

图9 分类数据集中数据分布情况A.各品系水稻抗性分布；B.不同抗性对应各类别病斑数量占比。R：抗病型；M：中感型；S：感病型；S＋：高感型。

表1 不同分类模型的分类结果

图10 高斯过程支持向量机分类结果混淆矩阵

图11 慢性型病斑特征

图12 人工标注和预测结果生成的病斑掩膜对比分析A.病斑标注和训练结果；B.人工标注掩膜和预测结果掩膜像素占比。

图13 冠层尺度水稻病斑识别分割面临的挑战1～2：病斑被遮挡的2种情况；3：虚焦导致病斑清晰度降低；4：水面高亮干扰病斑检测。

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